Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

7.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.9K
Data Collection by Experiments01:13

Data Collection by Experiments

25.4K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
25.4K
Crossover Experiments01:16

Crossover Experiments

3.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
3.7K
Experimental Designs01:16

Experimental Designs

15.6K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
15.6K
Quantifying Work02:30

Quantifying Work

21.8K
As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
21.8K
Social Exchange Theory01:26

Social Exchange Theory

22
As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...
22

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

European Multicentre Study on Recurrent Imaging Practice in Computed Tomography.

The British journal of radiology·2026
Same author

Spectrally Resolved Fundus Autofluorescence as a Biomarker of Retinal Metabolic Integrity in Intermediate and Atrophic AMD.

Investigative ophthalmology & visual science·2026
Same author

SAMIRA study on criteria for acceptability of medical radiological equipment (CARE). Survey results on European Union national legislations/regulations.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026
Same author

"If Immoral Then Unable": Asymmetric Generalizations in Social Judgment.

Personality & social psychology bulletin·2026
Same author

Performance tests of the B-RAD survey meter and portable γ-spectrometer in a clinical environment.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2025
Same author

True and Moral by Repetition: Unveiling the Impact of Exposure on Positive Stereotypes Perception.

International review of social psychology·2025

Related Experiment Video

Updated: Sep 24, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

607

EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability.

Andrea Tocchetti1, Lorenzo Corti1, Marco Brambilla1

  • 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.

Frontiers in Artificial Intelligence
|May 9, 2022
PubMed
Summary
This summary is machine-generated.

Explainable AI (XAI) research is crucial for understanding black-box models. Our gamified framework, EXP-Crowd, aims to improve user comprehension and crowdsourced data quality for AI explainability.

Keywords:
Explainable AIcrowdsourcingexplainabilitygame with a purposegamification

More Related Videos

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.2K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.5K

Related Experiment Videos

Last Updated: Sep 24, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

607
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.2K
The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.5K

Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Crowdsourcing

Background:

  • The proliferation of AI and black-box models necessitates methods for explaining their behavior.
  • Explainable AI (XAI) aims to make AI systems understandable to human users.
  • Existing approaches often lack user engagement and focus on the crowd's perspective.

Purpose of the Study:

  • To investigate if gamification can enhance user understanding of black-box AI models.
  • To improve the quality of crowdsourced data for XAI through user engagement.
  • To develop a gamified crowdsourcing framework (EXP-Crowd) for XAI.

Main Methods:

  • Designing and implementing a gamified crowdsourcing framework named EXP-Crowd.
  • Engaging users in gamified activities to collect data features for explainability.
  • Facilitating knowledge sharing between AI researchers and users within the framework.

Main Results:

  • Preliminary design of a game with a purpose (G.W.A.P.) for collecting entity features.
  • Demonstrated potential for gamified activities to improve user understanding and data quality.
  • Established a collaborative environment for users and AI researchers.

Conclusions:

  • Gamified crowdsourcing shows promise for advancing Explainable AI.
  • The EXP-Crowd framework offers a novel approach to collecting explainability-related data.
  • Further development is needed to refine the framework for specific XAI needs.